At first, organizations were focused on collecting their enterprise data. Now, the challenge is to leverage knowledge out of the data to bring intelligent insights for better decision-making. Numerous technologies and solutions promise to make the most of your data. Among them, we find Data Fabric and Data Mesh. While these concepts may seem similar, there are fundamental differences between these two approaches. Here are some explanations.
It is no secret that the immense volumes of data collected each day have many benefits for organizations. It can bring valuable customer insights so companies can personalize their offers and differentiate themselves from their competitors, for example. However, the growing number of digital uses creates an abundance of information that can be hard to exploit without a solid data structure.
According to Gartner’s forecasts, by 2024, more than 25% of data management solution vendors will provide full data structure support through a combination of their own and partner products, compared to less than 5% today.
In this context, there are several avenues that can be explored, but two stand out the most: Data Fabric and Data Mesh.
What is a Data Fabric?
The concept of a Data Fabric was introduced by Gartner back in 2019. The renowned institute describes a Data Fabric as the combined use of multiple existing technologies to enable metadata-driven implementation and augmented design.
In other words, a Data Fabric is an environment in which data and metadata are continuously analyzed for continuous enrichment and optimal value. But beware! A Data Fabric is not a finished product or solution – It is a scalable environment that relies on the combination of different solutions or applications that interact with each other to refine the data.
A Data Fabric relies on APIs and “No Code” technologies that allow synergies to be created between various applications and services. These solutions thus enable the data to be transformed to extract the quintessence of knowledge throughout its life cycle.
What is Data Mesh
The concept of Data Mesh was introduced by Zhamak Dehghani of Thoughtworks in 2018. It is a new approach to data architecture, a new mode of organization, based on meshing data. Data Mesh is based on the creation of a multi-domain data structure. Data is mapped, identified, and reorganized according to its use, its target, or its potential exploitation. Data Mesh is based on these fundamental principles: the data owner, self-service, and interoperability. These three principles enable the creation of decentralized data management. The advantage? Bringing about interactions between different disparate data domains to generate ever more intelligence.
The key differences between Data Fabric and Data Mesh
To fully understand the differences between Data Fabric and Data Mesh, let’s start by discussing what brings them together. In both cases, there is no such thing as a “ready-to-use” solution.
Where a Data Fabric is based on an ecosystem of various data software solutions, Data Mesh is a way of organizing and governing data. In the case of Data Mesh, data is stored in a decentralized manner in their respective domains. Each node has local storage and computing power, and no single point of control is required for operation.
With a Data Fabric, on the other hand, data access is centralized with clusters of high-speed servers for networking and high-performance resource sharing. There are also differences in terms of data architecture. For example, Data Mesh introduces an organizational perspective, independent of specific technologies. Its architecture follows a domain-centric design and product-centric thinking.
Although they have different rationales, Data Mesh and Data Fabric serve the same company objectives of making the most of your data assets. In this sense, despite their differences, they should not be considered opposites but rather complementary.
